Physical function monitoring (PFM) plays a crucial role in healthcare
especially for the elderly. Traditional assessment methods such as the Short
Physical Performance Battery (SPPB) have failed to capture the full dynamic
characteristics of physical function. Wearable sensors such as smart wristbands
offer a promising solution to this issue. However, challenges exist, such as
the computational complexity of machine learning methods and inadequate
information capture. This paper proposes a multi-modal PFM framework based on
an improved TimeMAE, which compresses time-series data into a low-dimensional
latent space and integrates a self-enhanced attention module. This framework
achieves effective monitoring of physical health, providing a solution for
real-time and personalized assessment. The method is validated using the NHATS
dataset, and the results demonstrate an accuracy of 70.6% and an AUC of 82.20%,
surpassing other state-of-the-art time-series classification models.

本研究论文提出了一种基于改进的 TimeMAE 的多模态 PFM 框架，通过将时间序列数据压缩为低维潜在空间并集成自增强注意力模块，实现了对物理健康状态的有效监测，为实时和个性化评估提供了解决方案。该方法通过使用 NHATS 数据集进行验证，结果显示准确率达到 70.6%，AUC 达到 82.20%，超过其他最先进的时间序列分类模型。